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Author of the blog post here.

We submitted multiple results using various Cloud TPU v3 Pod slice sizes to show the current achievable Transformer training efficiency at several scales:

https://mlperf.org/training-results-0-6

We're actively improving the whole TPU software stack, so training efficiency is likely to continue to increase over time.

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What I'm really asking is: how much effective compute throughput are you able to get during Transformer training relative to the amount of theoretical raw compute available?

That's a great question. I don't have that analysis handy, but it would definitely be worth doing.

What about energy? (watt-hours?)

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